The present invention relates to an agreement breach prediction system, an agreement breach prediction method and an agreement breach prediction program.
Service level agreements (hereafter, “SLAs”) that are concluded between customers and service providers such as ISPs, ASPs, SaaS providers and the like, have attracted attention in recent years. For instance, SLAs are agreements that guarantee, for example, the quality of a service that is provided to a customer by a service provider; for instance, the response time of the service does not exceed 3 seconds according to the agreements. It is important for service providers that have entered into SLAs to detect the occurrence of SLA breaches in advance, and deal with the breaches before they occur. Non-Patent Document 1 below (first chapter) discloses a technology for predicting the occurrence of SLA breaches.    Non-Patent Document 1: P. DOMINGOS/M. PAZZANI co-authors, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss”, Machine Learning, 29, pp. 103-130, 1997 Kluwer Academic Publishers, Manufactured in The Netherlands
In Non-Patent Document 1, there is calculated a probability Pr(x|Pi) of an SLA breach within a certain period of time, upon occurrence of an event Pi, on the basis of the number of times that an SLA breach occurs within a certain period of time after the occurrence of a given event Pi, and the occurrence of an SLA breach is predicted using that probability. If SLA breaches that occur within a certain period of time are few, the calculated probability Pr(x|Pi) in this method fluctuates significantly, and prediction accuracy decreases when SLA breaches increase or decrease by just one breach. In a conventional simple Bayesian filter, events are handled as discrete values, and the occurrence of an SLA breach is predicted on the basis of whether a threshold value is exceeded or not. Therefore, this precludes continuous handling of parameter values that denote service quality, and finely-tuned prediction of the occurrence of SLA breaches on the basis of breach probabilities that are dissimilar for each parameter value.